Hacker News Reader

Comment by aspaceman | Hacker News Reader

It still hits the problems, but it can still be better than single-threaded. That's the point of his comment. People say the GIL is bad so they throw the baby out with the bathwater and no longer use threads, which isn't very well-reasoned.

Been a while since I've used Python, but as far as I remember the GIL only affects Python objects. So if you use Numpy for operations, you can avoid the GIL.

View on HN

> as far as I remember the GIL only affects Python objects. So if you use Numpy for operations, you can avoid the GIL.

Correct.

Threads cannot help Python-heavy code; they can help I/O-heavy or native-heavy code (assuming native code releases the GIL).

Even without multithreading, numpy programming can give amazing speedups. It's the reason why data science Python isn't dead in the water.

Can you give an example for your last sentence?

Here's a link: https://scipy-cookbook.readthedocs.io/items/ParallelProgramm... And a quote in italics below.

And, more importantly for us, while numpy is doing an array operation, python also releases the GIL. Thus if you tell one thread to do:

  print "%s %s %s %s and %s" %( ("spam",) *3 + ("eggs",) + ("spam",) )
  A = B + C
  print A

During the print operations and the % formatting operation, no other thread can execute. But during the A = B + C, another thread can run - and if you've written your code in a numpy style, much of the calculation will be done in a few array operations like A = B + C. Thus you can actually get a speedup from using multiple threads.

Reply on news.ycombinator.com